Growing Degree Day: Noninvasive Remotely Sensed Method to Monitor Diet Crude Protein in Free-Ranging Cattle

Abstract

The daily nutritional balance of free-ranging cattle is the net result of intake from available forage biomass and nutritive value weighed against the nutritional requirements of the animal. Plant phenology influences nutritive value. Plant phenology is dictated by time of year and an accumulation of photosynthetically active days. Growing degree day (GDD) is a concept that quantifies this relationship and has been used to predict nutritive value in perennial range grasses. GDD could be substituted for chemical analysis to inform grazing animal nutritional monitoring efforts. We hypothesized that in C4 grass-dominated rangelands, a cumulative GDD calculation would correlate with diet crude protein (CP) predictions obtained by fecal near infrared spectroscopy (FNIRS) from free-ranging cattle. Therefore, the objectives of our research were to evaluate the effectiveness of GDD to predict FNIRS-derived determinations of grazing cattle diet CP in 1) two groups of three individual animals grazing a small native pasture and 2) large commercial-scale herds grazing expansive rangelands. For the first objective, cumulative GDD and FNIRS-predicted diet CP were strongly correlated (r2 = 0.76; P < 0.01). Relationships between cumulative GDD and FNIRS-predicted diet CP for the second objective varied considerably among ranches, ranging from a low r2 of 0.05 (P = 0.871) to a high r2 of 0.78 (P < 0.049). Similar values for individual ranch/year combinations were stronger; ranging from a minimum r2 of 0.44 (P = 0.556) to a maximum of 0.95 (P = 0.051). The aggregate relationship between GDD and FNIRS-predicted CP for all ranch/year combinations was highly significant (r2 = 0.37; P < 0.001), but the standard error was 1.86% CP. The noninvasive remotely sensed grazing animal nutritional monitoring method described here was accurate enough to inform tactical rangeland diet quality assessments but was not accurate enough to inform operational-scale grazing management decisions.